{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-27T14:45:20.189000+08:00",
     "start_time": "2021-11-27T06:45:20.171Z"
    }
   },
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ArgumentError: Package CSV not found in current path:\n- Run `import Pkg; Pkg.add(\"CSV\")` to install the CSV package.\n",
     "output_type": "error",
     "traceback": [
      "ArgumentError: Package CSV not found in current path:\n- Run `import Pkg; Pkg.add(\"CSV\")` to install the CSV package.\n",
      "",
      "Stacktrace:",
      " [1] require(into::Module, mod::Symbol)",
      "   @ Base ./loading.jl:871",
      " [2] eval",
      "   @ ./boot.jl:360 [inlined]",
      " [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
      "   @ Base ./loading.jl:1094"
     ]
    }
   ],
   "source": [
    "using LinearAlgebra\n",
    "using Plots\n",
    "using CSV\n",
    "using DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[toc]\n",
    "## TMM basic algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-27T14:45:08.167000+08:00",
     "start_time": "2021-11-27T06:44:58.510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#== We define convert functions here==#\n",
    "function abs_epsilon(ϵr)\n",
    "\t#Input relative permittivity, output abs permittivity\n",
    "\treturn ϵr * ϵ0 \n",
    "end \n",
    "\n",
    "function abs_mu(μr)\n",
    "\treturn μr * μ0 \n",
    "end\n",
    "\n",
    "c = 2.998e8\n",
    "ϵ0 = 8.8542e-12\n",
    "μ0 = 4π* 1e-7\n",
    "n0 = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-27T14:45:08.748000+08:00",
     "start_time": "2021-11-27T06:45:00.006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rTM (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function tTM(n1, d1, λ0, ϵr)\n",
    "\tϵ1 = abs_epsilon(ϵr)\n",
    "\tcoff = 2sqrt(ϵ0/μ0)\n",
    "\tϕ = 2π * n1 * d1 / λ0\n",
    "\tdeno_real_part = sqrt(ϵ0*μ0) * ( (ϵ1 + n1)/ϵ1 ) * cos(ϕ) + sin(ϕ)\n",
    "\tdeno_im_part = √(ϵ0*μ0) * sin(ϕ) + (ϵ1 / n1) / sqrt(ϵ0*μ0)\n",
    "\t\n",
    "\tcoff * (1 / complex(deno_real_part, deno_im_part))\n",
    "end\n",
    "\n",
    "\n",
    "function rTM(n1, d1, λ0, ϵr)\n",
    "\tϵ1 = abs_epsilon(ϵr)\n",
    "\tϕ = 2π * n1 * d1 / λ0\n",
    "\tAre = cos(ϕ) * √(ϵ0 / μ0) \n",
    "\tBre = n1 * √(μ0 * ϵ0) * cos(ϕ) / ϵ1\n",
    "\tAim = sin(ϕ) * √(ϵ0 / μ0)\n",
    "\tBim = (ϵ1 / n1) * sin(ϕ) / √(ϵ0 * μ0)\n",
    "\trTM = (complex(Are, Aim) - complex(Bre, Bim)) / (complex(Are, Aim) + complex(Bre, Bim))\n",
    "\n",
    "\trTM\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-27T14:45:08.879000+08:00",
     "start_time": "2021-11-27T06:45:00.598Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rTE (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function tTE(n1, d1, λ0, ϵr)\n",
    "\tϵ1 = abs_epsilon(ϵr)\n",
    "\tϕ = 2π * n1 * d1 / λ0\n",
    "\tdeno_real_part = 2cos(ϕ)\n",
    "\tdeno_im_part = sin(ϕ) * (n1 * √(ϵ0 * μ0) / ϵ1 + ϵ1 / n1 / √(μ0 * ϵ0))\n",
    "\ttTE = 2 / complex(deno_real_part, deno_im_part)\n",
    "\n",
    "\ttTE\n",
    "\n",
    "end\n",
    "\n",
    "\n",
    "function rTE(n1, d1, λ0, ϵr)\n",
    "\tϵ1 = abs_epsilon(ϵr)\n",
    "\tϕ = 2π * n1 * d1 / λ0\n",
    "\tfactor = im(n1 * √(ϵ0 * μ0) / ϵ1 - ϵ1 / n1 / √(μ0 * ϵ0)) * sin(ϕ)\n",
    "\tdeno_real_part = 2cos(ϕ)\n",
    "\tdeno_im_part = sin(ϕ) * (n1 * √(ϵ0 * μ0) / ϵ1 + ϵ1 / n1 / √(μ0 * ϵ0))\n",
    "\trTE = factor / complex(deno_real_part, deno_im_part)\n",
    "\n",
    "\trTE\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wavelength Spectrum Scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-27T14:45:09.197000+08:00",
     "start_time": "2021-11-27T06:45:09.049Z"
    }
   },
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "UndefVarError: DataFrame not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: DataFrame not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope",
      "   @ In[5]:35",
      " [2] eval",
      "   @ ./boot.jl:360 [inlined]",
      " [3] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)",
      "   @ Base ./loading.jl:1094"
     ]
    }
   ],
   "source": [
    "\n",
    "# Calculate and Draw \n",
    "\n",
    "function calculate_TMorTE_series(\n",
    "\t\t\t\t\tλ_serise, \n",
    "\t\t\t\t\ttransmethod, \n",
    "\t\t\t\t\tϵr_series, \n",
    "\t\t\t\t\tn_series, \n",
    "\t\t\t\t\tthickness)\n",
    "\tseries_reparts = []\n",
    "\tif transmethod == \"tTM\"\n",
    "\t\tTransFunction = tTM\n",
    "\telseif transmethod == \"tTE\"\n",
    "\t\tTransFunction = tTE\n",
    "\telseif transmethod == \"rTE\" \n",
    "\t\tTransFunction = rTE\n",
    "\telse\n",
    "\t\tTransFunction  = rTM\n",
    "\tend \n",
    "\tprintln(TransFunction)\n",
    "\tseries_complex  = (@. (TransFunction(\n",
    "\t\t\t\t\t\t\tn_series,\n",
    "\t\t\t\t\t\t\tthickness, \n",
    "\t\t\t\t\t\t\tλ_serise,\n",
    "\t\t\t\t\t\t\tϵr_series))^2)\n",
    "\tfor cmplx ∈ series_complex \n",
    "\t\tpush!(series_reparts, real(cmplx))\n",
    "\n",
    "\tend \n",
    "\tseries_reparts\n",
    "end\n",
    "\n",
    "# Call data\n",
    "path = \"./indexdata/W-Refractive Index.csv\"\n",
    "DataFrame() \n",
    "\n",
    "df = CSV.read(path, DataFrame)\n",
    "wavelengths = df[!, 1]*1e-6\n",
    "ns = df[!, 2]\n",
    "ϵrs = df[!, 3]\n",
    "#ITMs =  (@. (tTM(ns, 50e-9, wavelengths, ϵrs))^2)\n",
    "TMreparts = calculate_TMorTE_series(ns, \"tTM\", wavelengths, ϵrs, 50e-9)\n",
    "TEreparts = calculate_TMorTE_series(ns, \"tTE\", wavelengths, ϵrs, 50e-9)\n",
    "#RTEreparts = calculate_TMorTE_series(ns, \"rTE\", wavelengths, ϵrs, 50e-9 )\n",
    "\n",
    "\n",
    "#plot(wavelengths, TEreparts, label=\"TE\",\n",
    "\t#\txlim=(0.2, 5))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thickness Scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0e-9:1.0e-10:2.09e-8\n",
      "tTM\n",
      "tTE\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# at 1.5μm:\n",
    "fixed_λ = 1.5\n",
    "ds = 1e-10 * range(10, length=length(ns))\n",
    "println(ds)\n",
    "TMreparts_d = calculate_TMorTE_series(ns, \"tTM\", fixed_λ, ϵrs, ds)\n",
    "TEreparts = calculate_TMorTE_series(ns, \"tTE\", fixed_λ, ϵrs, ds)\n",
    "#RTEreparts = calculate_TMorTE_series(ns, \"rTE\", wavelengths, ϵrs, 50e-9 )\n",
    "plot(ds, \n",
    "\tTMreparts,\n",
    "\tseriestype= :path,\n",
    "\tmarkershape= :star7,\n",
    "\tmarkercolor = \"green\",\n",
    "\tmarkeralpha=0.2,\n",
    "\tgrid=false,\n",
    "\tlinecolor=\"green\",\n",
    "\ttitle=\"transmittance of W(TM and TE)\" ,\n",
    "\tlabel=\"TM\",\n",
    "\tlegend=:topleft, yaxis=\"Y1\",\n",
    "\tylim = (0, 0.5e-22),\n",
    "\t#xlim=(0.2, 5) * 1e-9,\n",
    "\txlabel=\"thickness:m\",\n",
    "\tylabel=\"transmittance\",\n",
    "\tfmt = :png,\n",
    ")\n",
    "yaxis!(\"T-TM\")\n",
    "plt = twinx()\n",
    "plot!(plt, ds, \n",
    "\tTEreparts,\n",
    "\tygrid=false,\n",
    "\tseriestype= :path,\n",
    "\tmarkercolor = \"blue\",\n",
    "\tmarkeralpha=0.2,\n",
    "\tlabel=\"TE\",\n",
    "\tlagend=:topright, yaxis=\"T-TE\",\n",
    "\t#ylim = (0, 0.5e-22),\n",
    "\t#xlim=(0.2, 5) * 1e-9,\n",
    "\txlabel=\"thickness:m\",\n",
    "\tylabel=\"transmittance\",\n",
    "\tfmt = :png,\n",
    ")"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Material Properties Fitting\n",
    "We use some self-defined methods to deal with the given data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Fitting $n - \\lambda$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting $k - \\lambda$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting $(k, n) - \\lambda$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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    "waves = df[!, 1]\n",
    "ns = df[!, 2]\n",
    "k = df[!, 3]\n",
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